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Raeesa Ganey

Publications and source records attributed to Raeesa Ganey.

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moveEZ: An R Package for Animated Biplots

The moveEZ (pronounced move easy) R package provides tools for constructing animated PCA biplots that reveal how multivariate structure evolves across the ordered levels of a categorical variable. Built as an extension to the biplotEZ package, moveEZ offers three animation frameworks of increasing methodological complexity: a fixed variable frame, in which variable vectors remain constant and only sample positions are animated; and two dynamic frames, in which both sample positions and variable vectors are recomputed and animated at each level. The dynamic frames support Procrustes alignment and reflection to ensure visual continuity across levels, and are compatible with high-dimensional datasets including grouped structures. The package integrates with gganimate to produce high-quality animations suitable for publications and presentations, and supports both animated and static faceted displays via a single argument. Although originally motivated by tracking shifts in African climate indicators, moveEZ is domain-agnostic and applicable wherever multivariate measurements are recorded repeatedly across an ordered categorical variable, including economic, ecological, and biological settings.

stat.CO

A Canonical Variate Analysis Biplot based on the Generalized Singular Value Decomposition

Canonical Variate Analysis (CVA) is a multivariate statistical technique and a direct application of Linear Discriminant Analysis (LDA) that aims to find linear combinations of variables that best differentiate between groups in a dataset. The data is partitioned into groups based on some predetermined criteria, and then linear combinations of the original variables are derived such that they maximize the separation between the groups. However, a common limitation of this optimization in CVA is that the within cluster scatter matrix must be nonsingular, which restricts the use of datasets when the number of variables is larger than the number of observations. By applying the generalized singular value decomposition (GSVD), the same goal of CVA can be achieved regardless on the number of variables. In this paper we use this approach to show that CVA can be applied and graphical representations to such data can be constructed. Specifically, we will be looking at the construction of a CVA biplot for such data that will display observations as points and variables as axes in a reduced dimension. Finally, we present experimental results that confirm the effectiveness of our approach.

stat.CO